Title :
On the training distortion of vector quantizers
Author_Institution :
Dept. of Math. & Stat., Queen´´s Univ., Kingston, Ont., Canada
fDate :
7/1/2000 12:00:00 AM
Abstract :
The in-training-set performance of a vector quantizer as a function of its training set size is investigated. For squared error distortion and independent training data, worst case type upper bounds are derived on the minimum training distortion achieved by an empirically optimal quantizer. These bounds show that the training distortion can underestimate the minimum distortion of a truly optimal quantizer by as much as a constant times n-1/2, where n is the size of the training data. Earlier results provide lower bounds of the same order
Keywords :
optimisation; vector quantisation; VQ; in-training-set performance; independent training data; lower bounds; minimum distortion; minimum training distortion; optimal quantizer; squared error distortion; training data size; training distortion; training set size; vector quantizers; worst case type upper bounds; Algorithm design and analysis; Computational efficiency; Councils; Mathematics; Source coding; Statistics; Testing; Training data; Upper bound; Vector quantization;
Journal_Title :
Information Theory, IEEE Transactions on
